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SCANPY: large-scale single-cell gene expression data analysis

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TLDR
This work presents Scanpy, a scalable toolkit for analyzing single-cell gene expression data that includes methods for preprocessing, visualization, clustering, pseudotime and trajectory inference, differential expression testing, and simulation of gene regulatory networks, and AnnData, a generic class for handling annotated data matrices.
Abstract
Scanpy is a scalable toolkit for analyzing single-cell gene expression data. It includes methods for preprocessing, visualization, clustering, pseudotime and trajectory inference, differential expression testing, and simulation of gene regulatory networks. Its Python-based implementation efficiently deals with data sets of more than one million cells ( https://github.com/theislab/Scanpy ). Along with Scanpy, we present AnnData, a generic class for handling annotated data matrices ( https://github.com/theislab/anndata ).

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Dimensionality reduction for visualizing single-cell data using UMAP.

TL;DR: Comparing the performance of UMAP with five other tools, it is found that UMAP provides the fastest run times, highest reproducibility and the most meaningful organization of cell clusters.
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SARS-CoV-2 entry factors are highly expressed in nasal epithelial cells together with innate immune genes.

TL;DR: In this paper, the expression of viral entry-associated genes in single-cell RNA-sequencing data from multiple tissues from healthy human donors was investigated, and co-detected these transcripts in specific respiratory, corneal and intestinal epithelial cells, potentially explaining the high efficiency of SARS-CoV-2 transmission.
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SARS-CoV-2 Receptor ACE2 Is an Interferon-Stimulated Gene in Human Airway Epithelial Cells and Is Detected in Specific Cell Subsets across Tissues.

Carly G. K. Ziegler, +135 more
- 28 May 2020 - 
TL;DR: The data suggest that SARS-CoV-2 could exploit species-specific interferon-driven upregulation of ACE2, a tissue-protective mediator during lung injury, to enhance infection.
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Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression

TL;DR: It is proposed that the Pearson residuals from “regularized negative binomial regression,” where cellular sequencing depth is utilized as a covariate in a generalized linear model, successfully remove the influence of technical characteristics from downstream analyses while preserving biological heterogeneity.
Journal ArticleDOI

The single-cell transcriptional landscape of mammalian organogenesis

TL;DR: A cell atlas of mouse organogenesis provides a global view of developmental processes occurring during this critical period, including focused analyses of the apical ectodermal ridge, limb mesenchyme and skeletal muscle.
References
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